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SIBGRAPI
2008
IEEE

Crop Type Recognition Based on Hidden Markov Models of Plant Phenology

10 years 4 months ago
Crop Type Recognition Based on Hidden Markov Models of Plant Phenology
This work introduces a Hidden Markov Model (HMM) based technique to classify agricultural crops. The method recognizes different crops by analyzing their spectral profiles over a sequence of satellite images. Different HMMs, one for each of the considered crop classes, are used to relate the varying spectral response along the crop cycles with plant phenology. The method assigns for a given image segment the crop class whose corresponding HMM presents the highest probability of emitting the observed sequence of spectral values. Experiments were conducted upon a sequence of 12 previously classified LANDSAT images. The performance of the proposed multitemporal classification method was compared to that of a monotemporal maximum likelihood classifier, and the results indicated a remarkable superiority of the HMM-based method, which achieved an average of no less than 93% accuracy in the identification of the correct crop, for sequences of data containing a single crop class.
P. B. C. Leite, Raul Queiroz Feitosa, A. R. Formag
Added 01 Jun 2010
Updated 01 Jun 2010
Type Conference
Year 2008
Where SIBGRAPI
Authors P. B. C. Leite, Raul Queiroz Feitosa, A. R. Formaggio, Gilson Alexandre Ostwald Pedro da Costa, Kian Pakzad, I. D. A. Sanches
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